Deep reinforcement learning for transportation network combinatorial optimization: A survey
Q Wang, C Tang - Knowledge-Based Systems, 2021 - Elsevier
Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …
combinatorial optimization problems, have attracted considerable attention for decades of …
A deep reinforcement learning algorithm using dynamic attention model for vehicle routing problems
B Peng, J Wang, Z Zhang - … ISICA 2019, Guangzhou, China, November 16 …, 2020 - Springer
Recent researches show that machine learning has the potential to learn better heuristics
than the one designed by human for solving combinatorial optimization problems. The deep …
than the one designed by human for solving combinatorial optimization problems. The deep …
A deep reinforcement learning approach for solving the traveling salesman problem with drone
Reinforcement learning has recently shown promise in learning quality solutions in many
combinatorial optimization problems. In particular, the attention-based encoder-decoder …
combinatorial optimization problems. In particular, the attention-based encoder-decoder …
Dimes: A differentiable meta solver for combinatorial optimization problems
Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …
Neural combinatorial optimization with reinforcement learning
This paper presents a framework to tackle combinatorial optimization problems using neural
networks and reinforcement learning. We focus on the traveling salesman problem (TSP) …
networks and reinforcement learning. We focus on the traveling salesman problem (TSP) …
Learning improvement heuristics for solving routing problems
Recent studies in using deep learning (DL) to solve routing problems focus on construction
heuristics, whose solutions are still far from optimality. Improvement heuristics have great …
heuristics, whose solutions are still far from optimality. Improvement heuristics have great …
Learning combinatorial optimization algorithms over graphs
The design of good heuristics or approximation algorithms for NP-hard combinatorial
optimization problems often requires significant specialized knowledge and trial-and-error …
optimization problems often requires significant specialized knowledge and trial-and-error …
Deep reinforcement learning for traveling salesman problem with time windows and rejections
R Zhang, A Prokhorchuk… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
Recently deep reinforcement learning has shown success in solving NP-hard combinatorial
optimization problems such as traveling salesman problems, vehicle routing problems, job …
optimization problems such as traveling salesman problems, vehicle routing problems, job …
Learning heuristics for the tsp by policy gradient
The aim of the study is to provide interesting insights on how efficient machine learning
algorithms could be adapted to solve combinatorial optimization problems in conjunction …
algorithms could be adapted to solve combinatorial optimization problems in conjunction …
Vehicle routing problem using reinforcement learning: Recent advancements
In the realization of smart cities, the most important component is the smart logistics in which
the vehicle routing problem (VRP) plays a significant role. The VRP has been proven to be …
the vehicle routing problem (VRP) plays a significant role. The VRP has been proven to be …